Integrating Background Knowledge for Scalable Causal Discovery

Abstract

Expert background knowledge is often available in practical applications of causal discovery. Such constraints on the true causal graph can help causal discovery in terms of identifiability of causal effects and accuracy of the learned structure, but also in reducing the space of candidate causal graphs. As causal discovery can become computationally expensive for large number of variables, it is crucial to utilize background knowledge effectively during the causal discovery process. However, most current methods only use background knowledge in a postprocessing step after causal discovery to refine the learned graph. In this work, we develop a framework for utilizing background knowledge during the causal discovery process, focusing especially on scalable causal discovery methods that recover only a subset of the whole graph. We implement our framework for multiple algorithms and empirically show that utilizing background knowledge can both reduce computational requirements and increase the quality of the learned structures.

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Cite

BibTeX RIS

BibTeX

@misc{schubert2026integrating,
  title = {{Integrating Background Knowledge for Scalable Causal Discovery}},
  author = {Mátyás Schubert and Theofanis Aslanidis and Tom Claassen and Sara Magliacane},
  howpublished = {arXiv},
  year = {2026},
  month = nov,
  doi = {10.48550/arxiv.2607.10456},
  eprint = {2607.10456},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2607.10456},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - Integrating Background Knowledge for Scalable Causal Discovery
AU  - Mátyás Schubert
AU  - Theofanis Aslanidis
AU  - Tom Claassen
AU  - Sara Magliacane
PY  - 2026
DA  - 2026/11/05
PB  - arXiv
DO  - 10.48550/arxiv.2607.10456
UR  - https://arxiv.org/abs/2607.10456
AB  - Expert background knowledge is often available in practical applications of causal discovery. Such constraints on the true causal graph can help causal discovery in terms of identifiability of causal effects and accuracy of the learned structure, but also in reducing the space of candidate causal graphs. As causal discovery can become computationally expensive for large number of variables, it is crucial to utilize background knowledge effectively during the causal discovery process. However, most current methods only use background knowledge in a postprocessing step after causal discovery to refine the learned graph. In this work, we develop a framework for utilizing background knowledge during the causal discovery process, focusing especially on scalable causal discovery methods that recover only a subset of the whole graph. We implement our framework for multiple algorithms and empirically show that utilizing background knowledge can both reduce computational requirements and increase the quality of the learned structures.
ER  -

Abstract

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